{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "universal-satisfaction",
   "metadata": {},
   "source": [
    "# 排气温度预测"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleasant-trauma",
   "metadata": {},
   "source": [
    "# 一、获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "better-harrison",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================\n",
    "# get_data.py\n",
    "#=====================\n",
    "\"\"\"从大数据接口获取数据（包括天气数据和设备数据）\"\"\"\n",
    "import requests\n",
    "\n",
    "#按Fn+F12查看设备具体信息\n",
    "start_time = '2020-01-01 00:00:00'\n",
    "end_time = '2021-03-18 00:00:00'\n",
    "station_id = 'PARK569_EMS01'\n",
    "equip_id = 'ECR01'\n",
    "BIGDATA_DOMAIN = 'http://bigdata-platapi.fnwintranet.com'\n",
    "BIGDATA_USERKEY = \"a95c34cf34deb5a2d0af84f3aea2a616_algorithm-engine-flask\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "closing-somewhere",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_iv_data(startTime, endTime, equipID):\n",
    "    tags = {\n",
    "        \"equipID\": equipID,\n",
    "        \"equipMK\": 'ECR',\n",
    "        \"staId\": station_id\n",
    "    }\n",
    "    d = {\n",
    "        \"dataSource\": \"EMS\",\n",
    "        \"endTime\": endTime,\n",
    "        \"isClean\": False,\n",
    "        \"listQueries\": [\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.TcprAirOut\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.TcwIn\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.Pelec\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.STecr\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.IratioCpr\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m1h-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.TcwOut\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.TchwIn\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.TchwOut\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.PArefEva\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.DURrun\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.PAlube\",\n",
    "                \"tags\": tags\n",
    "            },\n",
    "            {\n",
    "                \"aggregator\": \"avg\",\n",
    "                \"downsample\": \"15m-avg-none\",\n",
    "                \"explicitTags\": True,\n",
    "                \"metric\": \"EMS.PArefCond\",\n",
    "                \"tags\": tags\n",
    "            }\n",
    "\n",
    "        ],\n",
    "        \"startTime\": startTime,\n",
    "        \"userKey\": BIGDATA_USERKEY\n",
    "    }\n",
    "    # 天气数据\n",
    "    w = {\n",
    "        \"params\": [\n",
    "            {\n",
    "                \"cityId\": 440304,\n",
    "                \"endTime\": end_time,\n",
    "                \"hourInterval\": 0,\n",
    "                \"startTime\": start_time\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "\n",
    "    url = BIGDATA_DOMAIN + '/internal/bigdata/time_series/get_history'\n",
    "    r = requests.post(url, json=d)\n",
    "    url_w = BIGDATA_DOMAIN + \"/internal/bigdata/new/weather/hour/data\"\n",
    "    r_w = requests.post(url_w, json=w)\n",
    "    return r.json(), r_w.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "robust-reducing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取数据并查看\n",
    "iv_data, w_data = get_iv_data(start_time, end_time, equip_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "horizontal-breathing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大数据平台的数据类型<class 'dict'>，字典的键dict_keys(['msg', 'retCode', 'data'])\n"
     ]
    }
   ],
   "source": [
    "print(\"大数据平台的数据类型%s，字典的键%s\"%(type(iv_data),iv_data.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-consistency",
   "metadata": {},
   "source": [
    "# 二、数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "suitable-canyon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1614358800', '1597872600', '1595475900', '1613464200', '1608426900']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=====================\n",
    "# data_processing.py\n",
    "#=====================\n",
    "import pandas as pd\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# 处理数据,并保存到data.csv文件里,data为字典格式，键为时间戳，值为排气温度\n",
    "data = iv_data['data'][0].get('dps')\n",
    "display(list(data.keys())[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "voluntary-entertainment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('1577808000', 23.898666666666664), ('1577808900', 23.85066666666666), ('1577809800', 23.812), ('1577810700', 23.776), ('1577811600', 23.74133333333334)]\n"
     ]
    }
   ],
   "source": [
    " # 从上面可以看出，字典无序,按照时间戳(字典的键)排序\n",
    "data = sorted(zip(data.keys(), data.values()))\n",
    "print(data[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "posted-chicken",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TcprAirOut</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00</th>\n",
       "      <td>23.898667</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:30:00</th>\n",
       "      <td>23.812000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:45:00</th>\n",
       "      <td>23.776000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01 01:00:00</th>\n",
       "      <td>23.741333</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     TcprAirOut\n",
       "2020-01-01 00:00:00   23.898667\n",
       "2020-01-01 00:15:00   23.850667\n",
       "2020-01-01 00:30:00   23.812000\n",
       "2020-01-01 00:45:00   23.776000\n",
       "2020-01-01 01:00:00   23.741333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 取出排气温度值\n",
    "gas_temp = [i[1] for i in data]  \n",
    "# 将时间戳变为时间序列\n",
    "gas_time = [time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(int(i[0]))) for i in data]\n",
    "# 将数据变为Dataframe的格式便于后续处理\n",
    "gas_data = pd.DataFrame(gas_temp, index=gas_time, columns=['TcprAirOut'])\n",
    "display(gas_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "referenced-nature",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PAlube</th>\n",
       "      <th>DURrun</th>\n",
       "      <th>PArefEva</th>\n",
       "      <th>TchwOut</th>\n",
       "      <th>TchwIn</th>\n",
       "      <th>TcwOut</th>\n",
       "      <th>IratioCpr</th>\n",
       "      <th>STecr</th>\n",
       "      <th>Pelec</th>\n",
       "      <th>TcwIn</th>\n",
       "      <th>TcprAirOut</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01 00:00:00</th>\n",
       "      <td>450.313333</td>\n",
       "      <td>445.426667</td>\n",
       "      <td>11634.88</td>\n",
       "      <td>443.113333</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:15:00</th>\n",
       "      <td>450.586667</td>\n",
       "      <td>445.700000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:30:00</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>23.820000</td>\n",
       "      <td>23.812000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 00:45:00</th>\n",
       "      <td>450.653333</td>\n",
       "      <td>445.733333</td>\n",
       "      <td>11634.88</td>\n",
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       "      <td>23.776000</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-01 01:00:00</th>\n",
       "      <td>450.506667</td>\n",
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       "      <td>18.422000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.728667</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                         PAlube      DURrun  PArefEva     TchwOut     TchwIn  \\\n",
       "2020-01-01 00:00:00  450.313333  445.426667  11634.88  443.113333  17.934667   \n",
       "2020-01-01 00:15:00  450.586667  445.700000  11634.88  443.420000  17.954667   \n",
       "2020-01-01 00:30:00  450.713333  445.786667  11634.88  443.506667  17.966667   \n",
       "2020-01-01 00:45:00  450.653333  445.733333  11634.88  443.486667  17.982000   \n",
       "2020-01-01 01:00:00  450.506667  445.700000  11634.88  443.360000  17.998667   \n",
       "\n",
       "                        TcwOut  IratioCpr  STecr  Pelec      TcwIn  TcprAirOut  \n",
       "2020-01-01 00:00:00  18.312667        0.0    NaN    0.0  23.931333   23.898667  \n",
       "2020-01-01 00:15:00  18.347333        0.0    NaN    0.0  23.872667   23.850667  \n",
       "2020-01-01 00:30:00  18.375333        0.0    NaN    0.0  23.820000   23.812000  \n",
       "2020-01-01 00:45:00  18.395333        0.0    NaN    0.0  23.772667   23.776000  \n",
       "2020-01-01 01:00:00  18.422000        0.0    NaN    0.0  23.728667   23.741333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# X的特征名称\n",
    "ECR_01_name = [\"TcwIn\", \"Pelec\", \"STecr\", \"IratioCpr\", \"TcwOut\", \"TchwIn\", \"TchwOut\", \"PArefEva\", \"DURrun\", \"PAlube\",\n",
    "               \"PArefCond\"]\n",
    "\n",
    "# 从iv_data中取出所有特征数据，并合并为一个大的dataframe\n",
    "for i in range(1, len(iv_data['data'])):\n",
    "    x = iv_data['data'][i].get('dps')\n",
    "    x = sorted(zip(x.keys(), x.values()))\n",
    "    # 取出特征值\n",
    "    x1 = [i[1] for i in x]  \n",
    "    # 对应的索引\n",
    "    x1_index = [time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(int(i[0]))) for i in x]  # 将时间戳变为时间序列\n",
    "    gas_data1 = pd.DataFrame(x1, index=x1_index, columns=[ECR_01_name[i - 1]])\n",
    "    gas_data = pd.merge(gas_data1, gas_data, left_index=True, right_index=True, how='outer')\n",
    "gas_data .to_csv('./gas_data .csv')\n",
    "display(gas_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "wrapped-convertible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查看weather的数据组成： {'predictTime': '2020-02-26 17:00:00.0', 'result': [{'cityId': '440304', 'time': '2020-02-26 17:00:00.0', 'hoursAhead': 0, 'weather': '晴', 'humidity': '69', 'pressure': '1015', 'realFeel': '27', 'pop': '10', 'qpf': '0.0', 'snow': '0', 'temp': '25', 'uvi': '1', 'windDegrees': '180', 'windDir': 'S', 'windSpeed': '7', 'windLevel': '2', 'sysTime': '2020-02-26 17:00:00.0'}]}\n",
      "\n",
      "天气的时间戳： ['2020-02-26 17:00:00.0', '2020-02-26 18:00:00.0', '2020-02-26 19:00:00.0']\n"
     ]
    }
   ],
   "source": [
    "'''将天气数据保存到w_data里'''\n",
    "weather = w_data['citys'][0].get('times')\n",
    "print(\"查看weather的数据组成：\",weather[0])\n",
    "# 取出天气的时间戳\n",
    "w_index = [i['predictTime'] for i in weather]\n",
    "print(\"\\n天气的时间戳：\",w_index[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "caroline-rough",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2020-02-26 17:00:00', '2020-02-26 18:00:00', '2020-02-26 19:00:00']\n"
     ]
    }
   ],
   "source": [
    "# 去掉时间日期后的\".0\"\n",
    "w_index = [i[:len(w_index[0]) - 2] for i in w_index] \n",
    "print(w_index[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "roman-procedure",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>windSpeed</th>\n",
       "      <th>uvi</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2020-02-26 17:00:00</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26 18:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26 19:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26 20:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26 21:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    windSpeed uvi humidity temp\n",
       "2020-02-26 17:00:00         7   1       69   25\n",
       "2020-02-26 18:00:00         6   1       75   24\n",
       "2020-02-26 19:00:00         5   1       80   22\n",
       "2020-02-26 20:00:00         5   1       85   21\n",
       "2020-02-26 21:00:00         4   1       85   22"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将天气的特征合并为一个大的Dataframe\n",
    "weather_name = [\"windSpeed\", \"uvi\", \"humidity\", \"temp\"]\n",
    "w = []\n",
    "for name in weather_name:\n",
    "    w.append([i['result'][0].get(name) for i in weather])\n",
    "w_data = pd.DataFrame(np.array(w).T, index=w_index, columns=weather_name)\n",
    "w_data .to_csv('./w_data .csv')\n",
    "display(w_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "gentle-machine",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 将天气数据与站点数据合并，由于站点数据为15min级的，天气为小时级的，因此，将天气数据扩充\n",
    "# gas_data = pd.merge(w_data, gas_data, left_index=True, right_index=True, how='outer')\n",
    "# print(gas_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fiscal-cornell",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 用相邻前面的特征值填补后面的 ，将小时级数据扩充\n",
    "# gas_data = gas_data.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "informational-armstrong",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 考虑将过去24h的排气温度入参\n",
    "AirOut_b24 = gas_data['TcprAirOut'].loc['2020-01-01 00:00:00':'2021-03-16 23:45:00']\n",
    "gas_data_a24 = gas_data.loc['2020-01-02 00:00:00':'2021-03-17 23:45:00']\n",
    "# 为了合并，将二者索引改为相同的\n",
    "AirOut_b24.index = gas_data_a24.index\n",
    "gas_data = pd.merge(AirOut_b24, gas_data_a24, left_index=True, right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "neutral-beauty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TcprAirOut_x</th>\n",
       "      <th>PAlube</th>\n",
       "      <th>DURrun</th>\n",
       "      <th>PArefEva</th>\n",
       "      <th>TchwOut</th>\n",
       "      <th>TchwIn</th>\n",
       "      <th>TcwOut</th>\n",
       "      <th>IratioCpr</th>\n",
       "      <th>STecr</th>\n",
       "      <th>Pelec</th>\n",
       "      <th>TcwIn</th>\n",
       "      <th>TcprAirOut_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-21 11:30:00</th>\n",
       "      <td>24.694667</td>\n",
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       "      <td>578.486667</td>\n",
       "      <td>12033.400667</td>\n",
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       "      <td>18.280000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.073333</td>\n",
       "      <td>24.780667</td>\n",
       "      <td>28.950667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21 17:30:00</th>\n",
       "      <td>26.222667</td>\n",
       "      <td>454.940000</td>\n",
       "      <td>436.333333</td>\n",
       "      <td>12039.291333</td>\n",
       "      <td>318.846667</td>\n",
       "      <td>10.586667</td>\n",
       "      <td>12.068667</td>\n",
       "      <td>19.460000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.633333</td>\n",
       "      <td>27.013333</td>\n",
       "      <td>36.872667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22 11:15:00</th>\n",
       "      <td>25.366000</td>\n",
       "      <td>512.273333</td>\n",
       "      <td>504.580000</td>\n",
       "      <td>12039.300000</td>\n",
       "      <td>493.713333</td>\n",
       "      <td>21.628667</td>\n",
       "      <td>21.999333</td>\n",
       "      <td>8.253333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.546667</td>\n",
       "      <td>24.346667</td>\n",
       "      <td>24.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22 15:15:00</th>\n",
       "      <td>40.074667</td>\n",
       "      <td>578.493333</td>\n",
       "      <td>553.053333</td>\n",
       "      <td>12043.157333</td>\n",
       "      <td>305.953333</td>\n",
       "      <td>10.022667</td>\n",
       "      <td>12.844667</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.113333</td>\n",
       "      <td>28.256000</td>\n",
       "      <td>39.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23 11:15:00</th>\n",
       "      <td>24.970000</td>\n",
       "      <td>664.786667</td>\n",
       "      <td>639.393333</td>\n",
       "      <td>12043.253333</td>\n",
       "      <td>450.333333</td>\n",
       "      <td>20.822667</td>\n",
       "      <td>24.578000</td>\n",
       "      <td>42.286667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.366667</td>\n",
       "      <td>25.404667</td>\n",
       "      <td>33.979333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TcprAirOut_x      PAlube      DURrun      PArefEva  \\\n",
       "2020-03-21 11:30:00     24.694667  592.493333  578.486667  12033.400667   \n",
       "2020-03-21 17:30:00     26.222667  454.940000  436.333333  12039.291333   \n",
       "2020-03-22 11:15:00     25.366000  512.273333  504.580000  12039.300000   \n",
       "2020-03-22 15:15:00     40.074667  578.493333  553.053333  12043.157333   \n",
       "2020-03-23 11:15:00     24.970000  664.786667  639.393333  12043.253333   \n",
       "\n",
       "                        TchwOut     TchwIn     TcwOut  IratioCpr  STecr  \\\n",
       "2020-03-21 11:30:00  494.860000  23.579333  25.036667  18.280000    1.0   \n",
       "2020-03-21 17:30:00  318.846667  10.586667  12.068667  19.460000    0.0   \n",
       "2020-03-22 11:15:00  493.713333  21.628667  21.999333   8.253333    1.0   \n",
       "2020-03-22 15:15:00  305.953333  10.022667  12.844667  39.666667    0.0   \n",
       "2020-03-23 11:15:00  450.333333  20.822667  24.578000  42.286667    1.0   \n",
       "\n",
       "                         Pelec      TcwIn  TcprAirOut_y  \n",
       "2020-03-21 11:30:00  29.073333  24.780667     28.950667  \n",
       "2020-03-21 17:30:00  36.633333  27.013333     36.872667  \n",
       "2020-03-22 11:15:00   5.546667  24.346667     24.970000  \n",
       "2020-03-22 15:15:00  74.113333  28.256000     39.916667  \n",
       "2020-03-23 11:15:00  76.366667  25.404667     33.979333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nan处理\n",
    "gas_data = gas_data.dropna()\n",
    "display(gas_data.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "creative-split",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 丢掉Pelec=0的行，功率为0说明没有启动，对训练无意义\n",
    "gas_data = gas_data.drop(index=gas_data.Pelec[gas_data.Pelec == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "consistent-salvation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 异常值处理(无异常值,暂不处理)\n",
    "# # 将异常值保存到abnormal_data.csv中\n",
    "# abnormal_data = gas_data.drop(index=gas_data.TcprAirOut[gas_data.TcprAirOut <= 46.26].index)\n",
    "# abnormal_data.to_csv('./abnormal_data.csv')\n",
    "# # 丢掉分位值95%以上的数据（45.93）\n",
    "# gas_data = gas_data.drop(index=gas_data.TcprAirOut[gas_data.TcprAirOut > 46.26].index)\n",
    "gas_data.to_csv('./data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "atmospheric-individual",
   "metadata": {},
   "source": [
    "# 三、训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stainless-provincial",
   "metadata": {},
   "source": [
    "## 3.1 XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "velvet-agent",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================\n",
    "# xgb_ml.py\n",
    "#=====================\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import pickle\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV, TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "duplicate-duration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'特征集：\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TchwOut</th>\n",
       "      <th>TchwIn</th>\n",
       "      <th>TcwOut</th>\n",
       "      <th>IratioCpr</th>\n",
       "      <th>Pelec</th>\n",
       "      <th>TcwIn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-21 11:30:00</th>\n",
       "      <td>494.860000</td>\n",
       "      <td>23.579333</td>\n",
       "      <td>25.036667</td>\n",
       "      <td>18.280000</td>\n",
       "      <td>29.073333</td>\n",
       "      <td>24.780667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-21 17:30:00</th>\n",
       "      <td>318.846667</td>\n",
       "      <td>10.586667</td>\n",
       "      <td>12.068667</td>\n",
       "      <td>19.460000</td>\n",
       "      <td>36.633333</td>\n",
       "      <td>27.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22 11:15:00</th>\n",
       "      <td>493.713333</td>\n",
       "      <td>21.628667</td>\n",
       "      <td>21.999333</td>\n",
       "      <td>8.253333</td>\n",
       "      <td>5.546667</td>\n",
       "      <td>24.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-22 15:15:00</th>\n",
       "      <td>305.953333</td>\n",
       "      <td>10.022667</td>\n",
       "      <td>12.844667</td>\n",
       "      <td>39.666667</td>\n",
       "      <td>74.113333</td>\n",
       "      <td>28.256000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23 11:15:00</th>\n",
       "      <td>450.333333</td>\n",
       "      <td>20.822667</td>\n",
       "      <td>24.578000</td>\n",
       "      <td>42.286667</td>\n",
       "      <td>76.366667</td>\n",
       "      <td>25.404667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TchwOut     TchwIn     TcwOut  IratioCpr      Pelec  \\\n",
       "2020-03-21 11:30:00  494.860000  23.579333  25.036667  18.280000  29.073333   \n",
       "2020-03-21 17:30:00  318.846667  10.586667  12.068667  19.460000  36.633333   \n",
       "2020-03-22 11:15:00  493.713333  21.628667  21.999333   8.253333   5.546667   \n",
       "2020-03-22 15:15:00  305.953333  10.022667  12.844667  39.666667  74.113333   \n",
       "2020-03-23 11:15:00  450.333333  20.822667  24.578000  42.286667  76.366667   \n",
       "\n",
       "                         TcwIn  \n",
       "2020-03-21 11:30:00  24.780667  \n",
       "2020-03-21 17:30:00  27.013333  \n",
       "2020-03-22 11:15:00  24.346667  \n",
       "2020-03-22 15:15:00  28.256000  \n",
       "2020-03-23 11:15:00  25.404667  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'输出：\\n'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2020-03-21 11:30:00    28.950667\n",
       "2020-03-21 17:30:00    36.872667\n",
       "2020-03-22 11:15:00    24.970000\n",
       "2020-03-22 15:15:00    39.916667\n",
       "2020-03-23 11:15:00    33.979333\n",
       "Name: TcprAirOut_y, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = FontProperties(fname=r'C:\\Windows\\Fonts\\simsun.ttc', size=12)\n",
    "data = pd.read_csv(r\".//data.csv\", sep=',', index_col=0)\n",
    "x = data.loc[:, ['TchwOut', 'TchwIn', 'TcwOut', 'IratioCpr', 'Pelec', 'TcwIn']]\n",
    "y = data['TcprAirOut_y']\n",
    "display(\"特征集：\\n\",x.head())\n",
    "display(\"输出：\\n\",y.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "protected-indication",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 时序数据，不可以随机划分训练集测试集，按先后时间段划分\n",
    "x_train = x.iloc[:4200]\n",
    "x_test = x.iloc[4200:]\n",
    "y_train = y[:4200]\n",
    "y_test = y[4200:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "domestic-button",
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgboost的训练,girgsearchcv调参\n",
    "def xgb_train(x_train, y_train):\n",
    "    # 这是时间序列的测试集和训练集分割函数\n",
    "    my_cv = TimeSeriesSplit(n_splits=2).split(x_train)\n",
    "    print(\"my_cv:\", my_cv)\n",
    "    cv_params = {'min_child_weight': [1,2,3,4,5,6,7]}\n",
    "    other_params = {'learning_rate': 0.1, 'n_estimators': 1000, 'max_depth': 4, 'min_child_weight': 2, 'seed': 0,\n",
    "                    'subsample': 0.8, 'colsample_bytree': 0.9, 'gamma': 0, 'reg_alpha': 0.1, 'reg_lambda': 2}\n",
    "    model = XGBRegressor(**other_params)\n",
    "    optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params, scoring='r2', cv=my_cv, verbose=1, n_jobs=4)\n",
    "    optimized_GBM.fit(np.array(x_train), np.array(y_train))\n",
    "    model = optimized_GBM.best_estimator_\n",
    "    print('参数的最佳取值：{0}'.format(optimized_GBM.best_params_))\n",
    "    print('最佳模型得分:{0}'.format(optimized_GBM.best_score_))\n",
    "    print(\"Features sorted by their score:\")\n",
    "    # 特征重要性按从大到小排序\n",
    "    l=sorted(zip(map(lambda x: round(x, 4), model.feature_importances_), x_train.columns),\n",
    "                 reverse=True)\n",
    "    # 饼图,explode（距离圆心的距离）突出显示，autopct=\"%.0f%%饼里加占比\n",
    "    x=[l[i][0] for i in range(len(l))]\n",
    "    names=[l[i][1] for i in range(len(l))]\n",
    "\n",
    "    explode = [0.1, 0.1, 0.08,  0, 0,0]\n",
    "    plt.pie(labels=names, x=x, autopct=\"%.2f%%\", explode=explode, shadow=True)\n",
    "    plt.title(u\"特征影响占比\",fontproperties=font)\n",
    "    plt.show()\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "incoming-transport",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_cv: <generator object TimeSeriesSplit.split at 0x0000023FB8EC4888>\n",
      "Fitting 2 folds for each of 7 candidates, totalling 14 fits\n",
      "参数的最佳取值：{'min_child_weight': 4}\n",
      "最佳模型得分:0.9285602550182264\n",
      "Features sorted by their score:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chengjingd\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:24: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.384353988253093\n",
      "r^2 0.9526252199727684\n"
     ]
    }
   ],
   "source": [
    "model = xgb_train(x_train, y_train)\n",
    "# 保存模型\n",
    "with open(r'.XGBModel.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "y_hat = model.predict(np.array(x_test))\n",
    "# 性能评价  R^2越小越好，最优值为1\n",
    "mse = mean_squared_error(y_test, y_hat)  # ((y_test-y_hat)**2).mean()\n",
    "print('MSE', mse)\n",
    "r_2 = r2_score(y_test, y_hat)\n",
    "print('r^2', r_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "brown-magnitude",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-21 10:45:00    37.197333\n",
      "2021-02-21 11:00:00    38.552000\n",
      "2021-02-21 11:15:00    39.378000\n",
      "2021-02-21 11:30:00    39.930000\n",
      "2021-02-21 11:45:00    40.351333\n",
      "                         ...    \n",
      "2021-02-23 15:30:00    40.395333\n",
      "2021-02-23 15:45:00    40.057333\n",
      "2021-02-23 16:00:00    38.239333\n",
      "2021-02-23 17:15:00    28.066667\n",
      "2021-02-23 17:30:00    39.333333\n",
      "Name: TcprAirOut_y, Length: 100, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"可视化\"\"\"\n",
    "plt.figure()\n",
    "t = len(x_test[:100])\n",
    "plt.plot(np.arange(t), y_hat[:100], 'c*-')\n",
    "plt.plot(np.arange(t), y_test[:100], 'm.-.')\n",
    "plt.title(u'排气温度的预测值', fontproperties=font)\n",
    "plt.legend(['y_real', 'y_pred'])\n",
    "# 标记异常点\n",
    "print(y_test[:100])\n",
    "y_diff = y_test[:100] - y_hat[:100]\n",
    "for idx, val in enumerate(y_diff):\n",
    "    if (val > 1):\n",
    "        plt.scatter(idx, y_hat[idx], c='red', edgecolors='black')\n",
    "        plt.annotate(r\"$this\\ is\\ a\\ Outlier,bias=$\" + str('%.2f' % val), xy=(idx, y_hat[idx]), xycoords='data',xytext=(+30, -30),\n",
    "                    textcoords='offset points', arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "impossible-budget",
   "metadata": {},
   "source": [
    "# 3.2 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "postal-reservoir",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================\n",
    "# forest_ml.py\n",
    "#=====================\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "def forest_train(x_train, y_train):\n",
    "    # 调用scikit-learn的网格搜索，传入参数选择范围，并且制定随机森林回归算法，cv = 5表示5折交叉验证\n",
    "    my_cv = TimeSeriesSplit(n_splits=2).split(x_train)\n",
    "    param_grid = {\"n_estimators\":[300,500,700],\"max_depth\":[20,50,80]}\n",
    "    grid_search = GridSearchCV(RandomForestRegressor(random_state=1), \n",
    "                               param_grid, cv=my_cv)\n",
    "    # 让模型对训练集和结果进行拟合\n",
    "    grid_search.fit(x_train, y_train)\n",
    "    print(\"最优模型为：\", grid_search.best_estimator_)\n",
    "    model = grid_search.best_estimator_\n",
    "    # model = RandomForestRegressor(max_depth=16, n_estimators=250,random_state=1)\n",
    "    #\n",
    "    # model.fit(x_train, y_train)\n",
    "    print(\"Features sorted by their score:\")\n",
    "    names = x_train.columns\n",
    "    l=sorted(zip(map(lambda x: round(x, 4), model.feature_importances_), names),\n",
    "                 reverse=True)\n",
    "    # 饼图,explode（距离圆心的距离）突出显示，autopct=\"%.0f%%饼里加占比\n",
    "    x=[l[i][0] for i in range(len(l))]\n",
    "    names=[l[i][1] for i in range(len(l))]\n",
    "\n",
    "    explode = [0.1, 0.1, 0.08,  0, 0,0]\n",
    "    plt.pie(labels=names, x=x, autopct=\"%.2f%%\", explode=explode, shadow=True)\n",
    "    plt.title(u\"特征影响占比\",fontproperties=font)\n",
    "    plt.show()\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "northern-standard",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优模型为： RandomForestRegressor(max_depth=20, n_estimators=300, random_state=1)\n",
      "Features sorted by their score:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.3557484978145718\n",
      "r^2 0.9561510811801813\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = forest_train(x_train, y_train)\n",
    "# 保存模型\n",
    "with open(r'.RandomForestRegressorModel.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "y_hat = model.predict(np.array(x_test))\n",
    "# 性能评价  R^2越小越好，最优值为1\n",
    "mse = mean_squared_error(y_test, y_hat)  # ((y_test-y_hat)**2).mean()\n",
    "print('MSE', mse)\n",
    "r_2 = r2_score(y_test, y_hat)\n",
    "print('r^2', r_2)\n",
    "\n",
    "\"\"\"可视化\"\"\"\n",
    "plt.figure()\n",
    "t = len(x_test[:100])\n",
    "plt.plot(np.arange(t), y_hat[:100], 'c*-')\n",
    "plt.plot(np.arange(t), y_test[:100], 'm.-.')\n",
    "plt.title(u'排气温度的预测值', fontproperties=font)\n",
    "plt.legend(['y_real', 'y_pred'])\n",
    "# 标记异常点\n",
    "y_diff = y_test[:100] - y_hat[:100]\n",
    "for idx, val in enumerate(y_diff):\n",
    "    if (val > 1):\n",
    "        plt.scatter(idx, y_hat[idx], c='red', edgecolors='black')\n",
    "        plt.annotate(r\"$this\\ is\\ a\\ Outlier,bias=$\" + str('%.2f' % val), xy=(idx, y_hat[idx]), xycoords='data',xytext=(+30, -30),\n",
    "                    textcoords='offset points', arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amber-genetics",
   "metadata": {},
   "source": [
    "## 3.3 BP网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "lesbian-birth",
   "metadata": {},
   "outputs": [],
   "source": [
    "#=====================\n",
    "# network_ml.py\n",
    "#=====================\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn import preprocessing\n",
    "# 神经网络对特征数据敏感，需要归一化\n",
    "scaler = preprocessing.StandardScaler().fit(x)\n",
    "x_net = scaler.transform(x)\n",
    "# 时序数据，不可以随机划分训练集测试集，按先后时间段划分\n",
    "x_train_net = x_net[:4200]\n",
    "x_test_net = x_net[4200:]\n",
    "y_train = y[:4200]\n",
    "y_test = y[4200:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "perceived-gentleman",
   "metadata": {},
   "outputs": [],
   "source": [
    "def net_train(x_train, y_train):\n",
    "    cv_params = {'hidden_layer_sizes': [(9,3)]}\n",
    "    other_params = {'hidden_layer_sizes': (9, 3), 'activation': 'relu', 'solver': 'adam', 'alpha': 0.001,\n",
    "                    'batch_size': 'auto',\n",
    "                     'learning_rate_init': 0.001, 'power_t': 0.5, 'max_iter': 5000,\n",
    "                    'shuffle': True,\n",
    "                    'random_state': 1, 'tol': 0.0001, 'verbose': False, 'warm_start': False, 'momentum': 0.9,\n",
    "                    'nesterovs_momentum': True,\n",
    "                    'early_stopping': False, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08}\n",
    "    model = MLPRegressor(**other_params)\n",
    "    optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params)\n",
    "    optimized_GBM.fit(np.array(x_train), np.array(y_train))\n",
    "    model = optimized_GBM.best_estimator_\n",
    "    print('参数的最佳取值：{0}'.format(optimized_GBM.best_params_))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "desirable-cutting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "参数的最佳取值：{'hidden_layer_sizes': (9, 3)}\n",
      "MSE 0.3030961270684888\n",
      "r^2 0.9626409175243937\n"
     ]
    }
   ],
   "source": [
    "model = net_train(x_train_net, y_train)\n",
    "# 保存模型\n",
    "with open(r'.MLPModel.pkl', 'wb') as f:\n",
    "    pickle.dump(model, f)\n",
    "y_hat = model.predict(x_test_net)\n",
    "mse = mean_squared_error(y_test, y_hat)  # ((y_test-y_hat)**2).mean()\n",
    "print('MSE', mse)\n",
    "r_2 = r2_score(y_test, y_hat)\n",
    "print('r^2', r_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dietary-sellers",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y5ERUA0vEh5j5u+XYp1Iaul5idaE9JKWSlCNrhQB8C8A+Zr6+9CJNXMppsel6idWF9pCUSlIO18pCAOcA+AQRPWK/PlmG/U4oxGJbtu5q3ProM1i27mqsXX9F0WKu6yVWF9pDUipJya4VZv4pACpDWSY0psUGwLDYriyq663rJVYX0kOS+gW0h6SUDx3ZmYNKBqguvfRSJFMNoFAIT+7fV3aLrb+/H/v3Po6jR49i/97HVcTHEO0hKZVEhTwLhbo7ChH9Sy+9FEM7d+ELX/0Gdj72LKZ0TVOf9jimv78f133xWty69Uosm3sMbt16pfaQlPLBzKP+mjdvHo8VQ0ND3Duzj0OhEPfO7OOhoSHfbXtn9vFVO3bx7ftfdF5X7djF7R2dGfsYGhri9u5pfNWOXbxzzwFru+5pvvtP1KfS9r16cDu3tHek/b6huZk7u6flVdZyn7uiKNUHgN3soallHRBU7RSaAuYVoHrjlZfwznv/gxXXXJ22jxBQkI/77UNvpe3746efgfePHsU/XHI+/vDe79E2tR01tWEsL1O6mqa/Kcr4ZUK5VgpNAfNK4fu/26/HX3/5HzP28cJzvynIx51I1mfsu3HKVNTU1ODo0aNIJJO45NobypaupulvijJ+mVBCXmgKmFeA6pXnDnjuIxyJFuTjPvfss3DDmoG0fd+wZgDnnn1WUWV14/bXP6npb4oybplQQl7oIBmvAFVHV7fnPhobGgrKSrjppptw1mf/Al9e9Xl8dk4Pvrzq8zjrs3+Bm266qaiymngFaaPxhAZTC6SahtRXU1mUKsTLcV7p11gFOwsNSBa6j3IGE0spqwRpVw9u567pvRwKhTjV3MKp5taSzn00GevAbDnulfFYFmVsgU+wc0IJOXN5BGK0RKbY44RCIV65dRu3dnanPfzRRLKsWTCVotzCVcx19MtY6p3ZV1QZSqGayqKMLSrkRTLWlmEx9M7s47bunsA+/IUKV7Y6KrZRkMZQejRd03t55dZtHAqFynqu+RAKhXjnngNp12PnngNjUhZlbPET8gmVflgoQU3Z27RxA84+++zABjcLCfTmqqNipz5om9qOoeu/iMu2bnP2e+O6lWib2l7GM80PHd6v5GJCBTsLJagpe/39/b5B2SA8/IUEenPVUbHZP4ePHMFlW7el7feyrdtw+MiREs+ucHR4f3Y0EKxCnpUgz1i39YvXFvzwV8sDUYhw5aqjYrN/Dr76iud+D776SjGnVBI6vN+fcs8aGli8/C2VfgUl2FmIr7YafemFlKnaMiPyLXuuOir2vOqiMc/91kVjZT9XpXgmWiAYGuws/KEeGBjghpbcKXvVJoLFEIQHwkvcBwYGMtIqU82tPDAwkPV3uWjv6MyY+6alvYPbOzoreYpKgUy0QLAKORduYbd3T+MzL17FXR/8EFMoxLFkfZpAFLPfQhhNK7/aHwi/xrJT6sjILjnz4lVlufYNzc2cam7humiMiYijiYRn/StjRxAMkHIyroU8X8ErRKwKuUEqIYKjbeV32seq1gfCrz6IqGINUCE9smpzq00UxkNvuBD8hDzw6YeFpAgWksZVSKCzEulh5V4xKBvDw8N48803cMOaAawe3O5cx3+8YjW+ct2XynqsYvGbiTISj+P/br8eD919J1749VPoOHY6FixaWpbsnLt+cg9WXbc9ax0ENUV1vKArYdl4qXulX+W0yAt1l7S6/J6t7R2erXfvzL68u+yFuGHyZTRdHeIPTit/IskNjY1lP1axeNVzW3cPL1i01MNH3lKyj5w5vzqohq699ggmDvCxyAOdfjg8PFzwEmmH//A/uHnTWnxu7jG4edNaHP7D/3hu9yefOBV333YrLtqwBbc++gwu2rAFd992K/7kE6dmbNvf349ZvR/C3bcN46KN1+A7jz6Dtdu+ie/92x1FryZUyqRZhRwHAN54800MXHsD+levww133Ivb9j6PtTd+C79/z/vaFJumWEp6o99MlC8deBqrB7fjtwdfxZo/Ow2bL/ws6qIxfO/7P3COWUx62vDwMGKJZM46GOsUVU2/UwAE1yIXK7iQoeiFWE9+PuPO7mmeZYkl68vWMxgaGuKGxkZONbc425x58SqOJeuZCrC68vUf+vmZiajofRZ6zu5te2f2MRFxoj7FFApxe0cnNzY3O4HHTjvQST7zytQ3tTj7KdRiNntY7v26z7WQnlslGOvjK6MLxluw05zhL9fDJhTirihE3Hpn9jEVsO9sjYRkSyQbGtMyJmKJJLd19zCFQtzW3cMNzc05xTNfEWuZ0ua5XcuUtqL3aTI0NMSxRNIRHCLiWH3K+pus95wbxRTROR89yfP8BwYGOJasz9qYF+OiMs9RZpCkUIgT9amMa55P+mMlIaKM+7+1s9vzPlX8KdU9NVruLT8hD6xrRbq0Hz/9DPSvXodvXbMRn5t7DP7hkvN9gx3tnV15uSuGh4d9F4oIR6KeZWme2pG3K8RvNaEXnvsNNm+5BgxCOBLBh+bOA4NRW1eHaCKBizdfh+88+gwu3nwdJteGsfZv1jnl9XJZ5NvtP3r0CG5ctzLNbXHjupWomTwSC5djPLF/H9545SWs/l+n4i/6OrH6f52KN155ydeVcOmll+LS1X+Nd3/3Nv77ju9hwaKlaGprx9qvfgPfeexZLO0/HxcuX45ILI5QKIQVf3UJVmwexEN334lLtnwZD/z4h3jikZ95nv/3vv8DnH/O2b6LfUjwq1AXlXndPn76Gbjh3+7Bdx59Bu+8fSjjvrrrJ/dg9eD2tKH8qwe3466f3OO7/3IST9bjki1fTjv+JVu+jHiyflSOX20U474bHh7GpatW4a133gUDeOudd3HpqlV5/7ajswuXrFw9tu4tL3Wv9KucFrm5eHFbdw8TUdqCyNJKtnd0ciQWz2o9yfZ10Rinmls8B4Q0NDZmtLztHZ2cbGjM2D7V3OrZMmcbNUihENdFY04Qr627J+v2fq6OgYGBjAWevaznoaEhJqKsM/2Zx2hqm5r3QBnT5RSJxS1rsaOLz7x4FTe1tXNNOMzRRDJtf9KzEUs6lkjmHGWZLXWyGFdQpVNPy2m9+fUEqUry/0cTq+eX4FiynmH39mKJRM7rW+zgL7m3Gqe0jZp7C+PFtWL6TyXHd+XWbWkVcebFqzgciXJ904iPua27h9u6e3wvuPnAi9812dic1p0PRyKOn3fl1m1OwxFNJBw3j+y7rbuHG5ubPc+hsbnZ88ZpbG62fMJEHKu3BDAUCmV183iJzpkXr+KGltY094RZ3k5DyPKZ8tYUyoaWKQW5YeRayjkQEScbLdeRl1uka3ovX7Vjl/MXRDndXLnEulDhXLx4cVp8QjJhFi9e7HmO+bph8ilroVRD1ky1EE8kPA21XNlXxU7HIPGJ0XRvjQshdz8EIthi7cnD1NrZnSEQoVAoqx/b9LnHbEvWeTDt1l0q3O2XL8Q/zsyOBW82EsmGRm7v6GQi4kgszrDFq2t6Lzd6+LAl+AkPkev64IfSrkdTWztHDR9zqrmFo4kEk91IZAsWisUux8gndiDCCSJu6eiyGjX7HKxeUQsnGxqZiJxrZ17rlNEIRWJxrm9sTjv/pWdd4Jx7oj7FAwMDBYm19FbM35vEkvWeDX6iPuV7T5558Spunpo7mFtu4S0kiDyeGRoacnTAbVDVhuuy/lbuafN3XR/8UE4hJiLu+uCHOGrEf3LdL6UyLoTcy50igiDiIpacu8vbagtKtqCYCJrbkhXrWURHjuG2InO5MOQ42VwZ0tuIxEes/GSjZambQhyORNIaMfMmdIttU1u7cw5yPpIzbjZO5s0v3cremX2OCC8964Kc1ovZ2CYbm5z6Wbl1GzdP7WAi4ppwmFvaO9J6Seb1NoO8NbW1HI5EnPIvWXaep7XsF1x0C7xlbWd3r3k1jjv3HGDYPSB3YyHBXL8Atkm5XSE6lYBFe0cnI4dR4keL7Rpx/87PNco80nCQ3WNUi7wATHFePbidw9GY5ZIwLNaQj9g2tbVn9WO7XQwibKZwuRsJtxWZrWtvumT8HnoRmmgiwcceN9cRrDkfPYlTzS2O+E6qqeFYIulktrj9zO4GyxTrWH0qQzibpkz1tehCoRA3tU3lZEMjR+MJ39iBKfxy7Kh9nq0dXc7xI7G4Ux7Lsk4612P14HZunpreyEjXVRo+d8MlvSUi4vaOzrSl7AYGBri5Lf3cpIF0X3uxnjq7p6Wlki496wIOR2OOP98vcyjfLKeEff3LZb1J994tJA0t/iJUjZQaN6iLxpz6KaTHIw2hn1Xt97vO7mlcEw5zJB73TT1Wi9wHd8qhPOjJxhGfs7gVxJKVB0/EoKmtPS2lD7YANDY3ez6MpgXV1TuTU80tGVbkzj0HeMGipVxbF3H23TKlzbkZZWKnprZ2jsTivg+epNMtWLSUUy2tvGDRUp5UU8OReMI5z1Rzi+OekHNusYXSdD2YVqfpPiG722g2WOZ1Mt0uvTP7uGVKG1MoxMnGJmc/7thBNJH0dMPA6LK2dnbzgkVLORyJOPtJpKyGSH4jgVC5NubnUl7zXPx6TiOiHc94wLJZ28yWIFtWv3X9Y3YjKe4gL3cYc6af1cv9I/v36hEUa72FXPU5Wn7yfIU3n+3KETeQeit07h15NguxquU+JyIrhjOKAedxIeTuQUDio3JbaKnm1gyfpdva9hIAtyDevv9F53dLz7rAEVTL55zIEEO3kIhlKwG+lvaODJ+w6Y8z89FXD27nRKrBET6xisORKBNRmnCYN5JYuWZXOxKLO41PzB5g43ZFua+LnEM4EnUyR6TnI353syu/ePFibmhpTbvOEmsQq1bKUd/YzPH6VEbja9anuych5aqLxpxjmGWXIKyXi0muZSQWT4unmKIXiVvZDWZvQcp71Y5djjvI3ROpCYd5aGjIw/3j7b4xex+mKysciRb1TBQ6hqEc5Cu8+W5XjriBBOTdwXivbDazfETEtXURz0bfz6ru7J7G0USSo3aGTL69gHJkK40LIWe2AlWmpeZ1Ey9Zdl7GA7t6cLvjQjDFwi1kkfhI+lIknnAs92g8kWbNucXQyy0j2QumIHhlfSxYtJQj9v7NsomAtnX3OEJSF405AioiZfrTTUHZuedAWmBRrNhYcsS14yfqZo9ELB1JPXT3KM68eJXjRkmz8Ik4HIk4g3nEelm5dVtaQ2T2sMyGrLYukubbl+sg5y4urqVnXeAZtJUeWySe4Fgi6Vj/fhZxZ/e0tJ6OBGOXLDsva2xAgtdisfs2Fna9FePHFbx8/tEsrrpKkK/w5rtdOeYVEvel9Fzl/nPXtdmQdHZPs+JM9nOer1Ut1rgYSTGXa/OqHbu4uW2q50C3UnodzONEyN0W+ZyFJ6dZxiIKpuVrfi4VbApyyGXNijVvPmiNU9rSUgJF9MSnZnavxM9rVphU+sqt2zKE1vSFu1MGYfxOhMSy7ps4Eo87N62ItOScm74+U8xMv28smcwQHa8HSnopDS1TnLLEXH5e08o3/dytnd0ctsVYymD2ptxxhtpwnWOxdk3v5Sld09JGdKaaW2zrKZFmkcfqUxmW0dKzLuDaOqsRCUeiTixFcvRjdtZKrD7luLCkey7HlPEEsUQy40G3eiVTuSYczggY+7lvpBGWcvoFmHPd/2YDKi6glJGK604zzde9ka+1mK/w5rtduTJ5ZEyHpB3ncjlJz9ZtQOUqg1mPZ168ihOpBq4N1430UOOZuevlOsdxIeSmj9x6yFrThHDl1m2OsMtDPmfhyRyJJzyDlu7/xRXgtkxFEM2Z9twPvDycrR7umcYpbY6IiUUq4iXumq7pvVwbrkvLKInE4o47Q0RGfOxi7Ur2x4JFSz0j6OKuMIXIcjWM+Kdl+xbbRy3i0tTWzhH7OpuNgfkbyRGXB0GunVwHaUjERy7WSzSRcD6Xv9F4whm0ZQUxk2miK/W8ZNl5TtZOfVOL0+CZjUM0nnCuiRk09RrQFInF03oOcg1qwmGOxq14gWn1mplEkvdvXlu/gKoc388qjyaSacFadx68u1fg7k02tbWnjZ24ascujhm51X5jCQpNYcx3fpd8xasc1qrXNcrVkDixsvp6n6wV715SJBrjcCRS0CDAcs1mOi6EXFIEI7GYY5GKEDS1TeWoYTlJ11weftNCd3f/5UYXS8ptfXVN73UsS0tY6h1Xh3nslvYOz9TCJcvOc8Qm2dCY1ojAEEX3b2vCYY4lktw0ZSrH61NcU1vLRMTNU0esS2msUs0tVoPh6tZHbCEyLQ7J4BGfsTyUk2pqnMaxti6S5ls3yyp51pKfXhcbEWuvAUBmHCMST3A4EuVJNTVpja6ZnSL+bD+3j/ScFixayuFo1CmnNHpNbe3O8aUn0dQ21ZnvRYLGpo+/LhrjY4+bmyHKUn6rER+x9BqntDl+fnNAWGtnt+3GaU2bFjiaSDrXOhpPZIwNkJ6kW8wGBgbSBqqZ96U7iyvmGsm7enB7mk/eb06ifCaIMy12a0K33PPLFCLQpfiPJfPE3XPyMqrkvAYGBjgciTgNvru+amprOZpIeI41EANM3HVOLCrunfY5NDSU1yjrfBgXQi6+yEg8ntYdkps4Xp9Ks4ZM37F5k1tCMJLOJ5UQicUdf7V50ecsPNkJWIpVbWbMpJpbuCYcdl5m0Mvxt8fiHK6LpLlWKBRyjjVn4ckZFpek60m+uCmsIlYrt27jWNISqNpwXYYQwbZszUZPXAY1tbVp/j0Rs1giyXE7pc90mUiKlljk8foG290x0pOIJpJ2PcRHXDI+KaFSfiJrlK5pvbsDn6ZoiTtEHj5p8MRP7XZfSMqjBMBNV4TUv/QA3ALb2tHFU7os37k04tIom+6u5qkdI26jUIi7ps/klGEdy3Vtae9wGowly85zehvZ0iK93Hni0pJzFBeb2wAxr72fFe12c5kBeLe7wiu9NZsoye/NWSuzibSfoGcTenP6DbNs5vgJM0Dd0NjoZIeFI5GMup00aXLGJG2xRNIRaemRmAH/xiltvtNUSCPjHv9QdT5yAEsBPAHgVwD+Ntf2xQq5BKNgd4fMKUzlwTK7/TJAIBJPcE1t2BHvqJHOZ1ZWTW2tY4maLbTbMpWMiKa2qU73WvYjVldtOJzZWNhWpljdUt4ly85zBNB94yUbGh3LwD3gQbJEJPIugmg++O50xlgy6Vwrr5zsSDzB9Y3N3DilLc3vbKYGxpL1jhiJj1FSO8ORCNeEw471bgZC3d1LedDitntCrGb38dz+Tql/sxewZNl5XBMOc6qpxUghtB4cSZ20GtZk2shNkB3MTiQ9p8S1GvZ4Wt2LC0gE0N2jEEvPNBzEapdsGLHaHReYTzaT6W/v6p2ZYQWmmlucAK3XaGYzI8gvvc5sQNPdMklube/IGFlbiJugUKvcb96ghub0Z9XM4a+LxpxcbrPnYT5r5sA7Cap3ffBDGYJcEw47hom7AYh4DHpzl9Pd2DQ2Nxv1ndt6z0XFhBzAJAC/BvABALUAHgXQl+03pbhWJPF/waKlzgCAMy9e5QiZVI4MHrGCXZEREbe7XyPpfBFnSPzkmhrHmjOtNq8RY+LacIuvY90bwUi5Ad1BldWD2x1hkZsukUrPVZYc7d6ZfWmZISK84uOTbBsz7VIEThqlWH09N05pc4K37qBca0dXmr9ZMnbEZSRCI+co19IMPkrMwBRLERy30IhYizCaYijpmmZDKJaTu0dm+uWlR2XmfUuvRBoJM4MnEh8ZnddiDFxyrF7XNAjispOG2RQPGakq19ccxGSel1xXU+jN8QVmF988Rm1dxOl1pJpbnPiKmTJpDkyTZyPZ2Jw1va7RthgtV9VIVpQYPF6Wfr4WeSFz0ZjbLj3rAqe34iesYgG772X5rV/2kBM3cjVIVoObe5I25swegukCkzI2NDdzTTickeDgLn8hVFLIPwrgP4z36wGsz/abYoU81WRZVolUyvHlykNpCXwizbKY89GTuDZcl2YdiQVSEw5zbbguY/SkuBNMwTVdNM4DnhixbM1Kj9n5paD0dD2x7N2Wz7HHzc1wE3llMsjMbu7umYiECLAZM5Ab2XwozCwYrxTNSDzuXB9pjBKphoxgselekIAzhULO4Ct3LrU1L04krYsu11l+09rZzY1T2nyzOsJ1dWliblqxIpDRuDUq1l3eRntgk/TQRPjlHBqntHmOWpWG0nHT2GmichwxECSDRe4fM4YiPQjZv/Ridu45wE3tHY5bSHz4ZiBZAmpmFo28N+MQMgWC2QiEIxFOpDIDsqYVTUTc0DzSuIgrz8zm8HL5udNCvdwm5gAxr2wud0DVTCc17518Zr90W+QS6HY3jLFk0klccKdtyjOd7Vr5YSZiOD03O/biziSTHlyuSbm8qKSQ/zmAbxrvzwFwU7bfFCPkx82e7eRVi6VptrpSAe6b2cy5vn3/i87DYAqZNdhnxKcrFrFpgbpFyewmuYNjYqU6mS62leQeai9WplhWuaLmQ0PWykHSg4gl6zlZn7IeQDsrRrqK9U0tGcJkTombbGzimtrajPOaNHky19bVpfnbu6b3pu3LCjjH03okxx43l+tisbRjmA1IOBLluph1vWTVHxEYCR6avn+vB8lseECU9iC2dnQ5Q/XFfeSuF2lYTd98JBbneH0qbU4Y84Frsa38VHOrM6rWcVc1t6RlTe3cc4Dr7R6YuFJaO7qcxlauS1NbO0fshiASHxlj4I4LiOVr3leOX96+t+UY5mAtKb+UV3phXmLoTDNsB++kB+b0VkIjUzSYPUXJ9HFnybS2d1jWqe1Xl3vSbwi8GVAVMXTHC3IJq8x7Ij7yBYuWOsFK8/lzXEZ278ydtiluNnEluQ2JZJYh9+ZcTWbvXF7lmotlzIUcwAoAuwHs7u7uLqjwQ0NDjtAmG5vSuqUiuMmGJs9pas0u8O37X+RwNDYy85794JqDfVYPbue4LY7ykMjN4Q5MyZB788ETITMDaGaZrIybhL2fkUbIy3LIp6JD4h82Apdm0M0UJsl3NfO6w5FIWkD2qh27uMm2GmU/cpOabqLJNbVOCpY85LXhOsddZc7fIg2f26cog1mk+y+/Mf38puiY3edYfSpNRKU3IKIZdYmBKXax+pQ18ZjtIpFpdd1jEm7fPzIl8IJFSx2L2xzCb4qOBFbFspPGQuZpMa1TGU1ojkGQVEepMzP2ILNAyn7FfSR1KZ+bPbL6phant5NsbErLujIHvO3cc8Cpf6lzaVAlBdRdf2K9u5+3Y4+by6mmFkcMxbr2ErNko7V0n+meCEciac9iItWQlqLr1wiIkUNEViaT3Zs0B7+53StmTKIuGnNGTScbmzmerM/ondWG69KeXaKR0aIyV5PpTpSent+ArViyviAdZA64a0WmRJULb93EI1kHkp+cMPzVzix6sfQAqDwoUdtilm6heyrcRKrBcbV4tbR+KY4rt27jsD0QRR4Uv9ZYBNI9wCbXhD3ua+N0+2Mxx5KWbBbzBpJgpGNdiaVsNDwilOZ5e7k6zKwX6Z2I8CRSDWlR/2yTColPX8RH3DqewSZXANEMSJt1KYFQsytv+nrlIYvYaYDSE6gJhzMsTDOI5e7FScNupruJbz6asHpI8foGx0J0iz5EdOxsKas+RvypseRIJo7Uh/jJzQbIHMsgPTszSC0NkPQI3a45c2SxOfK2rbuHjz1uruegO/do4aa2qda8QEaswwwEe7nxzPRdczoJJ2HAHvxljt8we6tewUKpJyeV2Ccw63bXhEIhbmhpdRqNqCuGIQPSvCx8uUfM6yFBf/fsrGk9igJzyJkrK+STATwN4Bgj2Dkr228KFXIyrFe5CcxIs+nDdq88I5anCKU5Ck9Ery4ay8giEKvDPWovFApllEXynk2XgsyR4pdWFo0nnHTKVHOLp4DkO2TbHWSRbBO3u0bcUl6RfEnhM10V5jX1Ezgztcx9XSSw5Xcji/C7s4e8XBxi3Zp1ZApQJBrjRCplWbUua0uyN6R+xGKS47sF35w0zKwDr26yGcA1XR/iDqk1xh9IVo38NhJPcDSecASrLhrj+sZmp2ExJ0cTYWlomZLmjpPnQEamSrBfroHsp627JyMAHbZ/J8kDkbid6mpkU4k17Q52mha506Oxr30ilXKug5nK6LaAzQFi0ruC/XuZvtjtbvILrpoLzpiGmV/aZXtHZ9pzYyYkSCaU+OvDkYhj4bt7elKW9o7OtLEt4sL16+m5y58vlU4//CSAJ+3slQ25ti9UyBOubrTZKi9evNgz+8I9XNsc3WbmeZsZDuL/FP9zOBLJWM3HzKN2W9nitpDjSNDTT8TMfbtXHCpmVJvZRfUSs2wPhTsFzT297ZkXr7JcBj4DNjq7p/laTn6LO0vapvs7vzzlhsbGNFeQeykv6V77PTjy8Ir/1wyQmb5QSTNz0+mxrFdTW7tzndwxE3HZmY1SU1s7t9jZQTE7sCyppTLqU0a5gtJHpYrAuNNsJYtKsmVMizxki5P4gFs7u52cdmk8nFzz3plOxpHpWnOPkhY3jARDZXCU09OxA6ZmUFZGDcu4C3FPiNtSXGzSOIhhJEHqbOmObmMmXt/gDG6TxkLKMbmm1il7OBLhliltHApZS0HGEiOpqZKjb869JPXjZ+HL9XBnX5ViqLkJ9IAgqci0G8lu/Zk5Y9SUZA6YF3z14HYnL9ect7qhsdFxy6SN9rPzPCWh33QT+EWhvR5av7Qvce34iWqpeI3WExHymkDfS/ily5vPaDv5velyiNVbwcdsebdeD8bKrdsybnwZVJGPZTMwMOD42d0Pjrl6kdcshbFE0nm4vWbLSzY0ZszfnkilnPmp3RlS5vw87kZMrlusvt4RkEjccnWI60ca37TZKyk9zdZcrMQcYCYpn2ZAU6ZbrpUMG1fjLj0VU7RMF6KksUpg0AzCyqhdMzvKnLpZ3Hq1dkaUE0ey75MRS7bV2YfEHLLVu9eCM5JWLD2iY4+bmzYNg9dAH/HRm1NSSCMrjZ47XiaD06SsFApxix3gNo0EopF580tZpzXQQp5rzoaBgYGMdKVCRp9Z84D7LxrgXt3HbwFiLzeK2x1gCmeuaHwpeIlnQ3MzN7dNTQ+q2jdyPsO0s5GrUfIbmed33PaOzrTtO7unFTRda64h33JPuTNrco2+yzZS0Z0i6p6fx8u/Kz0ZERAicqZiENecOVBJ0tbc+zUzX+Z89CQnA6U2XJe273A0ag96sUTN3bjLOA3TjSBpqaZFLQ2OuIfIEDzp2ZqNg4i0TCNhir80+qZbUrKJHBdPizGEntKDjX4zoIprVBpI6SVkPLeu/HDJDHNedoaRX5xBDEAZsyJ16LVdrvVDcxFoIc9ndJi5DmNtXZ3PhDbek+AUMjOZDM91+3VjyaTnMmLif3S34CJOpYhnPtfNLWbZhkCXsvZjqSuR5+p2lnsBBb/YQqn7N4VegrbuTJFYPJ62favhQxfxkGCpGWwU607iGV4uHpkh0rz/ZVUqcVfIS9wOTVNGGveacNhJSzUbGPe4iEgsNjLuQixnu1ch1r7ZOIi7wbTCxXqXnofZWCVSqbTJ7mSwk9mYmAFSP2NA3DrippLeirkITF005jmXOoicQHGqudVp5LyOJddchF8serertlhLXAi0kDMXNqmOWDBmQLTFHvzhRTFDjtNTBevTumfmwr6LFy/2FapSxbPclGPiIr9eTanHrcSSZu7jlnuBhnyvibvHJ24fCQS6MyW8RluKFSpi7dUjdYup1xB10woO24PsxC1jDv66aofEmOJpSQdiyMiUzOasnuI/N7ePxOMZ+fRmbzYcjaal1Mo6sGYj5WW0yXPU2T1txO1h9FbMgU/yvZkFI4OMFixa6mT21NaNpEZ6xbxkYJWZYeeebqFUAi/kheBn6fpZVoXOFVyo4GXbvhTxrDYqeS65GtByUK45o93lLuSamFMxyMpKptiYS/C5/cIyD79XjMgMykUT1nw6uVLzzEm3ZOI4t0UqA4ZkOL9kcLmnHZZ54cnuMUuPQuYk8nI1dk3vdXoNYrGbaZZyLLO34jUiWgYvibVsDnzy67GbWTvmCF7fRUNicetYtrsoW1ykFCaMkIsV5NdC+/2mHKt3KJWl0o1eNdwHflMxuPOm3WU1M0xkdKmfuyESizkThflt027HJNKnqohniL+TJx7PLLMEgSWpQGbxNF02I5a9/9S+khopA6LEmhd/dK5elDRsMp20OfDJr9H2Gp0ciSecbBh3zEt839myt8phdEwYIfea8yDf1VfGi2WsFE813AdeUzH4zXNtumScrKFkkusiUU/BcTKxGht5ck2Nb3BX5tA23VleK+n4DewxFx83kcQE9/ZJe7oNvywic24fyY7JR5CZLXGV7cyBT9kaAL+Yj5XllvBNgTXni3Fnb5WDCSPkxazEUQ0Pr6KUghlDyDa3iVeqphnT8bL6s036JmLrtbKRH9kymPzKnKhPOYNsJHBpBkjN8rjXP5W0UUlD9PLHuxsA6dm7FyCRhtBPLyrhnjOZMEJejL97rLvTZlm0QVGKYWhoKC3rRqZ/cI+ONQ2afO59856UlYHMOEU0YWVrlYNsRtjAwIAz5F1SD71GXbd1Wwt0e10fM200nwWTs41HyFYPldSTCSPkhV7ISreglSq3orgx3QV+q+OYLsZi7v1sFnyp5CpPLJFIW0Mgn7zwbOcRMQLJXq6gYrWhkgaZn5CT9d3oMn/+fN69e3fF9j88PIzNW67BU0/sx/TeGdi0cQP6+/s9t500aRJuffQZTK6pcT47cvgwls09BkePHq1YGd3M6JuFZeuuxuwTFzqf7Xngfty69Urs3/v4qJVDCS7mPfRXiz6CS665PuN++uamNXjuwLMAqufeF4aHh7F2/RVYsXkQM+ctwL6HH8Itm9bgui9ei/7+fgwPD+OvLr0M0UQSrzx3ALV1dZg8uQaJhka88vxvMKWzG2+/+QbOP/cc3HTTTSWXp9quDwAQ0cPMPN/9+eSxKEyl6e/v9xVuN9N7Z2Dfww+l3fD7Hn4I03tnVKp4njz1xH7MnLcg7bOZ8xbgqSf2j2o5lOCyaeMGrF2/Bis2D+K1l17wvJ9efP4553213PuCPLObt1zpGGEi4unfX4NXidDU2IRDh97CW68fBDPj7d++WTYRB6rv+mTFy0yv9KvSeeSFUC0ujWpx8SjBRrr1flMmuGcMrIZ7v1qpxuuDieIjL4ZqCDJW402jBJd876dquPermWq7PirkAaDabhol2Oj9NP7wE/JxGexUFEUZj/gFO0NjURhFUZRyMDw8jBl9szBp0iTM6JuF4eHhsS7SmDAus1YURRn/eKUrrl2/BgDyzlobL6hrRVGUQDIRx16oa6WMaHdOUcYeHXsxggp5gUh3btm6q3Hro89g2bqrsXb9FSrmyoShWgwZGbBjUrUDdiqMCnmBbN5yDVZsHsTsExdick0NZp+4ECs2D2LzlmvGumiKUnGqyZDZtHEDbtm0BnseuB9HDh/Gngfuxy2b1mDTxg2jXpaxRn3kBVKN8y8oymhRbX7pQuZVGg9MqLlWKkmg5l9QlDJTbX7pQuZVGs+oa6VAtDunTGSK8UtXi099XOM13LPSr6AP0dehz8pEpdA5gXQOofICHaKvKEo5KMQvXW0+9aDj5yNXIVcUpWJockB50QFBiqKMOprrPTqokCuKUjE0OWB00PRDRVEqRq7l25TyoD5yRVGUgKA+ckVRlHGKCrmiKErAUSFXFEUJOCrkiqIoAUeFXFEUJeCUJOREdB0R7Seix4joe0SUKlO5FEVRlDwp1SL/MYDjmHkOgCcBrC+9SIqiKEohlCTkzPyfzHzEfvsAgM7Si6QoiqIUQjl95BcC+JHfl0S0goh2E9Hu1157rYyHrSw6l7KiKNVOziH6RHQXgDaPrzYw8w/sbTYAOAJgyG8/zHwLgFsAa2RnUaUdZWR9whWbBzFz3gLse/ghrF2/BgB0iLGiKFVDyUP0ieh8AH8JYBEzv5vPb4IyRF/nUlYUpZqoyBB9IloK4G8AfDpfEQ8S1bY+4URH3VyK4k2pPvKbACQA/JiIHiGim8tQpqpB51KuHsTNtWzd1bj10WewbN3VWLv+igkj5tqIKVnxWv+t0q+grNlZ6fUGde3P/Omd2cdX7djFt+9/0XldtWMX987sG+uiVRxd91IRoGt2Fkch6xMWul93IPWWTWt0rmYfJvKSYRqrUQRds7PK0IezMCby9ZrIjZiSjs5HXmWUK5A6UXynQVgyrFJ1UUqsZqLcHxMeL39LpV9B8ZFXknL4fCea77SaYwqVrIti9z3R7o+JAHx85CrkY0Q5HrKJHACsNipdF8U0Ynp/jD/8hFx95GNIqYFU9Z1WD9VYF9VYJqU01EdehfT392P/3sdx9OhR7N/7eMHZKprnXj1UY11UY5mUyqBCHmCCEACcKFRjXVRjmZQK4eVvqfRLfeTlo5oDgBONaqyLaiyTUjxQH7miKEqwUR+5oijKOEWFXFGUrOigoupHhVxRFF9Ga9ZJbSxKQ4VcURRfNm+5Bis2D2L2iQsxuaYGs09ciBWbB7F5yzVlO8ZEn6K4HGiwU1EUX0ZjUNFEnhCtUDTYqShKwYzGoCJdiat0VMgVRfFlNAYV6QjU0pk81gVQFKV6kWkjNm+50pkTqNyLn2zauAFr16/xXGRFyQ/1kSuKMuZUaiWu8YauEKQoihJwNNipKIoyTlEhVxRFCTgq5IqiKAFHhVxRFCXgqJAriqIEHBVyRVGUgKNCriiKEnBUyBVFUQKOCrmiKErAUSFXFEUJOCrkiqJUDF35Z3TQ2Q8VRakIsvKPOavh2vVrAEAnxCozOmmWoigVQVf+KT86aZaiKKOKrvwzeqiQK4pSEXTln9GjLEJORF8gIiai5nLsT1GU4DMay8QpFiUHO4moC8BpAH5TenEURRkvjMYycYpFycFOIroNwN8D+AGA+cx8MNdvNNipKIpSOBUJdhLR/wbwAjM/Wsp+FEVRlOLJ6VohorsAtHl8tQHAFbDcKjkhohUAVgBAd3d3AUVUFEVRslG0a4WIZgO4G8C79kedAF4EsICZX872W3WtKIqiFE7ZXSvMvIeZW5m5h5l7ADwP4IRcIq4oilJOdBoAHaKvKEqA0WkALHSIvqIogWWiTQOgQ/QVRRl36DQAFirkiqIEFp0GwEKFXFGUwKLTAFhosFNRlMCi0wBYaLBTURQlIGiwU1EUZZyiQq4oihJwVMgVRVECjgq5oihKwFEhVxRFCTgq5IqiKAFHhVxRlILQ2QarDx0QpChK3uhsg9WJDghSFCVvJtpsg9WGDghSFKVkdLbB6kSFXFGUvNHZBqsTFXJFUfJGZxusTjTYqShK3uhsg9WJBjsVRVECggY7FUVRxikq5IqiKAFHhVxRFCXgqJAriqIEHBVyRVGUgDMmWStE9BqAA0X+vBnAwTIWJwjoOU8M9JwnBqWc8zRmbnF/OCZCXgpEtNsr/WY8o+c8MdBznhhU4pzVtaIoihJwVMgVRVECThCF/JaxLsAYoOc8MdBznhiU/ZwD5yNXFEVR0gmiRa4oiqIYBErIiWgpET1BRL8ior8d6/KUAyLqIqJ7iGgvET1ORKvszxuJ6MdE9JT9t8H+nIhom30NHiOiE8b2DIqHiCYR0S+I6A77/TFE9KB9bjuJqNb+PGy//5X9fc+YFrxIiChFRLcR0X4i2kdEHx3v9UxEf23f178koluJqG481jMRfZuIXiWiXxqfFVy3RHSevf1TRHRevscPjJAT0SQA2wH8KYA+AMuIqG9sS1UWjgD4AjP3ATgRwIB9Xn8L4G5mng7gbvs9YJ3/dPu1AsDXRr/IZWMVgH3G+60AvsLMHwTwJoCL7M8vAvCm/flX7O2CyFcB3MnMMwDMhXXu47aeiagDwEoA85n5OACTAHwO47OedwBY6vqsoLolokYAVwL4CIAFAK4U8c8JMwfiBeCjAP7DeL8ewPqxLlcFzvMHABYDeALAVPuzqQCesP//OoBlxvbOdkF6Aei0b+5PALgDAMEaJDHZXd8A/gPAR+3/J9vb0VifQ4HnWw/gGXe5x3M9A+gA8ByARrve7gCwZLzWM4AeAL8stm4BLAPwdePztO2yvQJjkWPkphCetz8bN9hdyeMBPAhgCjO/ZH/1MoAp9v/j5TrcAOBvALxvv28C8FtmPmK/N8/LOWf7+7fs7YPEMQBeA/BPtjvpm0QUwziuZ2Z+AcAggN8AeAlWvT2M8V3PJoXWbdF1HiQhH9cQURzA7QBWM/Mh8zu2mudxk15ERKcDeJWZHx7rsowikwGcAOBrzHw8gHcw0tUGMC7ruQHA/4bViLUDiCHT/TAhqHTdBknIXwDQZbzvtD8LPERUA0vEh5j5u/bHrxDRVPv7qQBetT8fD9dhIYBPE9GzAL4Dy73yVQApIpLlB83zcs7Z/r4ewOujWeAy8DyA55n5Qfv9bbCEfTzX858AeIaZX2PmwwC+C6vux3M9mxRat0XXeZCE/GcAptsR71pYQZN/HeMylQwREYBvAdjHzNcbX/0rAIlanwfLdy6fn2tHvk8E8JbRfQsEzLyemTuZuQdWPf6Emc8CcA+AP7c3c5+zXIs/t7cPlOXKzC8DeI6Ieu2PFgHYi3Fcz7BcKicSUdS+z+Wcx209uyi0bv8DwGlE1GD3Zk6zP8vNWAcICgwmfBLAkwB+DWDDWJenTOf0x7C6XI8BeMR+fRKWb/BuAE8BuAtAo709wcre+TWAPbAyAsb8PEo4/1MA3GH//wEADwH4FYBdAML253X2+1/Z339grMtd5Ll+GMBuu66/D6BhvNczgKsB7AfwSwD/AiA8HusZwK2w4gCHYfW+LiqmbgFcaJ//rwBckO/xdWSnoihKwAmSa0VRFEXxQIVcURQl4KiQK4qiBBwVckVRlICjQq4oihJwVMgVRVECjgq5oihKwFEhVxRFCTj/HwFoe9tBU+zvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"可视化\"\"\"\n",
    "plt.figure()\n",
    "t = len(x_test[:100])\n",
    "plt.plot(np.arange(t), y_hat[:100], 'c*-')\n",
    "plt.plot(np.arange(t), y_test[:100], 'm.-.')\n",
    "plt.title(u'排气温度的预测值', fontproperties=font)\n",
    "plt.legend(['y_real', 'y_pred'])\n",
    "# 标记异常点\n",
    "y_diff = y_test[:100] - y_hat[:100]\n",
    "for idx, val in enumerate(y_diff):\n",
    "    if (val > 1):\n",
    "        plt.scatter(idx, y_hat[idx], c='red', edgecolors='black')\n",
    "        plt.annotate(r\"$this\\ is\\ a\\ Outlier,bias=$\" + str('%.2f' % val), xy=(idx, y_hat[idx]), xycoords='data',xytext=(+30, -30),\n",
    "                    textcoords='offset points', arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.show()\n",
    "\n",
    "\"\"\"test:y_test-y_pred是否大于0\"\"\"\n",
    "y_diff = y_test - y_hat\n",
    "plt.figure()\n",
    "t = len(x_test)\n",
    "plt.scatter(np.arange(t), y_diff, c='lightblue', edgecolors='black')\n",
    "plt.title(u'真值-预测值', fontproperties=font)\n",
    "plt.legend(['y_test-y_pred', 'y_pred'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "special-vegetarian",
   "metadata": {},
   "source": [
    "# 四、主程序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "integrated-floor",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#=====================\n",
    "# algorithm.py\n",
    "#=====================\n",
    "param = {\"x\": [362.64, 14.949333333333334, 16.139333333333333,\n",
    "               11.946666666666664, 15.866666666666667, 21.32933333333333], \"y\": 25.42}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "incorporated-great",
   "metadata": {},
   "outputs": [],
   "source": [
    "def temp_warning(temp_now, temp_max):\n",
    "    \"\"\"\n",
    "    判断当前温度是否超过温度上限\n",
    "    :param temp_now: 当前温度\n",
    "    :param temp_max: 温度上限\n",
    "    :return: null\n",
    "    \"\"\"\n",
    "    if temp_now > temp_max:\n",
    "        print(\"当前压缩机排气温度为%.2f；高于上限温度%.2f\" % (temp_now, temp_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "demanding-roots",
   "metadata": {},
   "outputs": [],
   "source": [
    "def temp_vary(data, t_start, t_end, percent):\n",
    "    \"\"\"\n",
    "    :param data: data['TcprAirOut']\n",
    "    :param t_start: 起始时间,如'2021-01-01 00:00:00'\n",
    "    :param t_end: 终止时间,如'2021-01-26 00:00:00'\n",
    "    :param percent: 分位值,如 95\n",
    "    :return: 从起始时间t_start到结束时间t_end的排气温度的分位值percent\n",
    "    \"\"\"\n",
    "    percent_val = np.percentile(data.loc[t_start:t_end], percent)\n",
    "    return percent_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "applicable-benjamin",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_evalute(y_test, y_hat, name):\n",
    "    '''\n",
    "    :param y_test: 实际排气温度\n",
    "    :param y_hat: 预测排气温度\n",
    "    '''\n",
    "    t = len(y_test)\n",
    "    fig, ax = plt.subplots(figsize=(15, 4))\n",
    "    # 设置 x 轴显示密度\n",
    "    tick_spacing = 25\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n",
    "\n",
    "    # 设置 x 坐标轴刻度的旋转方向和大小\n",
    "    # rotation: 旋转方向\n",
    "    plt.xticks(fontsize=9)\n",
    "    plt.yticks(fontsize=9)\n",
    "    plt.plot(new_data.index, y_hat, 'c*-')\n",
    "    plt.plot(new_data.index, y_test, 'm.-.')\n",
    "    plt.title(u'排气温度预测曲线：' + name, fontproperties=font)\n",
    "    plt.legend(['y_pred', 'y_real'])\n",
    "\n",
    "    # 标记异常点\n",
    "    y_diff = y_test - y_hat\n",
    "    for idx, val in enumerate(y_diff):\n",
    "        if (val > 1):\n",
    "            plt.scatter(idx, y_test[idx], c='red', edgecolors='black')\n",
    "            plt.annotate(r\"$this\\ is\\ a\\ Outlier,bias=$\" + str('%.2f' % val), xy=(idx, y_test[idx]), xycoords='data',\n",
    "                         xytext=(+30, -30),\n",
    "                         textcoords='offset points', arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "\n",
    "    plt.show()\n",
    "    # 性能评价  R^2越小越好，最优值为1\n",
    "    mse = mean_squared_error(y_test, y_hat)  # ((y_test-y_hat)**2).mean()\n",
    "    print('MSE', mse)\n",
    "    r_2 = r2_score(y_test, y_hat)\n",
    "    print('r^2', r_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "productive-split",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_model_warn(param, model, threshold, percent, name):\n",
    "    '''\n",
    "    第三层预测模型的告警设置\n",
    "    :param param: 实际测得的特征参数\n",
    "    :param model: 训练得到的模型\n",
    "    :param threshold: 允许误差的阈值\n",
    "    :param percent: 训练数据的90%分位数\n",
    "    :return: null\n",
    "    '''\n",
    "    y_real = param.get('y')\n",
    "    if (name == r'.MLPModel.pkl'):\n",
    "        y_pred = model.predict(scaler.transform(np.array([param.get('x')])))\n",
    "        print(\"真实值:%s   预测值:%s\" % (y_real, y_pred[0]))\n",
    "    else:\n",
    "        y_pred = model.predict(np.array([param.get('x')]))\n",
    "        print(\"真实值:%s   预测值:%s\" % (y_real, y_pred[0]))\n",
    "\n",
    "    if y_real > y_pred + threshold and y_real > percent:\n",
    "        print(\"警告!排气温度异常\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "strange-reset",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"显示排气温度曲线\"\"\"\n",
    "def display_temp(data):\n",
    "    x = np.arange(len(data['TcprAirOut_y']))\n",
    "    plt.figure(figsize=(30, 8))\n",
    "    plt.scatter(x, data['TcprAirOut_y'], c='blue', marker='x', s=40)  # 绘制散点图\n",
    "    # plt.plot(x, data['TcprAirOut_y'], 'b-', lw=1, linestyle='-')  # 绘制直线图\n",
    "    plt.xlabel('time', fontsize=10)  # X轴\n",
    "    plt.ylabel('TcprAirOut_y', fontsize=10)  # Y轴\n",
    "    plt.legend(['Data Point'])  # 曲线的标签\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "lucky-namibia",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载一组新的测试数据\n",
    "new_data = pd.read_csv(r\".//new_data.csv\", sep=',', index_col=0)\n",
    "# x = data.loc[:, \"windSpeed\":\"TcwIn\"]\n",
    "y_test = new_data['TcprAirOut_y']\n",
    "# x = data.loc[:, \"windSpeed\":\"TcwIn\"]\n",
    "x_test = new_data.loc[:, ['TchwOut', 'TchwIn', 'TcwOut', 'IratioCpr', 'Pelec',\n",
    "                          'TcwIn']]  # x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "charitable-coral",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-03-22 00:00:00至2021-03-15 00:00:00排气温度95分位值为:46.48\n",
      "2021-03-08 00:00:00至2021-03-15 00:00:00排气温度95分位值为:41.96\n",
      "*****************************************************\n",
      "Model:.RandomForestRegressorModel.pkl\n",
      "RandomForestRegressor(max_depth=20, n_estimators=300, random_state=1)\n",
      "[(0.7073, 'TcwIn'), (0.1515, 'TchwIn'), (0.0773, 'TchwOut'), (0.041, 'Pelec'), (0.0179, 'IratioCpr'), (0.0051, 'TcwOut')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.35008862154736464\n",
      "r^2 0.9363543070042963\n",
      "真实值:25.42   预测值:25.06040222222216\n",
      "*****************************************************\n",
      "{'TcwIn': 0.7073, 'TchwIn': 0.1515, 'TchwOut': 0.0773, 'Pelec': 0.041, 'IratioCpr': 0.0179, 'TcwOut': 0.0051}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:.XGBModel.pkl\n",
      "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "             colsample_bynode=1, colsample_bytree=0.9, gamma=0, gpu_id=-1,\n",
      "             importance_type='gain', interaction_constraints='',\n",
      "             learning_rate=0.1, max_delta_step=0, max_depth=4,\n",
      "             min_child_weight=4, missing=nan, monotone_constraints='()',\n",
      "             n_estimators=1000, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
      "             reg_alpha=0.1, reg_lambda=2, scale_pos_weight=1, seed=0,\n",
      "             subsample=0.8, tree_method='exact', validate_parameters=1,\n",
      "             verbosity=None)\n",
      "[(0.5495, 'TcwIn'), (0.1727, 'Pelec'), (0.1158, 'TchwIn'), (0.0631, 'TchwOut'), (0.0602, 'IratioCpr'), (0.0387, 'TcwOut')]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.18667639058518412\n",
      "r^2 0.9660624552942711\n",
      "真实值:25.42   预测值:25.11516\n",
      "*****************************************************\n",
      "{'TcwIn': 0.5494999885559082, 'Pelec': 0.17270000278949738, 'TchwIn': 0.11580000072717667, 'TchwOut': 0.06310000270605087, 'IratioCpr': 0.06019999831914902, 'TcwOut': 0.03869999945163727}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chengjingd\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:71: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:.MLPModel.pkl\n",
      "MLPRegressor(alpha=0.001, hidden_layer_sizes=(9, 3), max_iter=5000,\n",
      "             random_state=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE 0.2396861619998379\n",
      "r^2 0.9564253422046843\n",
      "真实值:25.42   预测值:27.2076234263824\n",
      "*****************************************************\n",
      "{'TcwIn': 0.5494999885559082, 'Pelec': 0.17270000278949738, 'TchwIn': 0.11580000072717667, 'TchwOut': 0.06310000270605087, 'IratioCpr': 0.06019999831914902, 'TcwOut': 0.03869999945163727}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chengjingd\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:71: MatplotlibDeprecationWarning: normalize=None does not normalize if the sum is less than 1 but this behavior is deprecated since 3.3 until two minor releases later. After the deprecation period the default value will be normalize=True. To prevent normalization pass normalize=False \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "import json\n",
    "if __name__ == '__main__':\n",
    "    \"\"\"----------------------------------------------\"\"\"\n",
    "    \"\"\"-------------第二层:趋势预测预警-----------------\"\"\"\n",
    "    \"\"\"----------------------------------------------\"\"\"\n",
    "\n",
    "    # 最近一个月的分位值\n",
    "    t_start_month = '2020-03-22 00:00:00'\n",
    "    t_end_month = '2021-03-15 00:00:00'\n",
    "    percent_month = 95\n",
    "    temp_month = temp_vary(data['TcprAirOut_y'], t_start_month, t_end_month, percent_month)\n",
    "    print(\"%s至%s排气温度%d分位值为:%.2f\" % (t_start_month, t_end_month, percent_month, temp_month))\n",
    "\n",
    "    # 最近一个周的分位值\n",
    "    t_start_week = '2021-03-08 00:00:00'\n",
    "    t_end_week = '2021-03-15 00:00:00'\n",
    "    percent_week = 95\n",
    "    temp_week = temp_vary(data['TcprAirOut_y'], t_start_week, t_end_week, percent_week)\n",
    "    print(\"%s至%s排气温度%d分位值为:%.2f\" % (t_start_week, t_end_week, percent_week, temp_week))\n",
    "\n",
    "    threshold = 1\n",
    "    if temp_week - temp_month > threshold:\n",
    "        print(\"警告!最近一周压缩机排气温度过高\")\n",
    "\n",
    "    # # 显示温度曲线\n",
    "    # display_temp(data)\n",
    "    print('*****************************************************')\n",
    "\n",
    "    \"\"\"----------------------------------------------\"\"\"\n",
    "    \"\"\"-------------第三层:叠加虚拟模型-----------------\"\"\"\n",
    "    \"\"\"----------------------------------------------\"\"\"\n",
    "\n",
    "    # 读取模型\n",
    "    l = [r'.RandomForestRegressorModel.pkl', r'.XGBModel.pkl', r'.MLPModel.pkl']\n",
    "    l_name = ['RandomForest', 'XGBOOST', '感知机']\n",
    "    # f_name = [131,132,133]\n",
    "\n",
    "    for idx, i in enumerate(l):\n",
    "        print(\"Model:\" + i)\n",
    "        with open(i, 'rb') as f:\n",
    "            model = pickle.load(f)\n",
    "        print(model)\n",
    "        if (i == r'.MLPModel.pkl'):\n",
    "            x_test = scaler.transform(x_test)\n",
    "        else:\n",
    "            l = sorted(zip(map(lambda x: round(x, 4), model.feature_importances_), x_test.columns),\n",
    "                       reverse=True)\n",
    "            print(sorted(zip(map(lambda x: round(x, 4), model.feature_importances_), x_test.columns),\n",
    "                         reverse=True))\n",
    "\n",
    "        y_hat = model.predict(np.array(x_test))\n",
    "\n",
    "        display_evalute(y_test, y_hat, l_name[idx])\n",
    "        predict_model_warn(param, model, threshold=0.1, percent=temp_week, name=i)\n",
    "        print('*****************************************************')\n",
    "        # 将特征占比输出到json文件里\n",
    "        d = {}\n",
    "        x = [l[i][0] for i in range(len(l))]\n",
    "        names = [l[i][1] for i in range(len(l))]\n",
    "        for i in range(len(x)):\n",
    "            d[names[i]] = np.array(x[i]).tolist()\n",
    "        print(d)\n",
    "        with open('features.json', 'w') as f:\n",
    "            json.dump(d, f)\n",
    "            # 饼图,explode突出显示，autopct=\"%.0f%%饼里加占比\n",
    "        x = [l[i][0] for i in range(len(l))]\n",
    "        names = [l[i][1] for i in range(len(l))]\n",
    "\n",
    "        explode = [0.1, 0.1, 0.08, 0, 0, 0]\n",
    "        plt.pie(labels=names, x=x, autopct=\"%.2f%%\", explode=explode, shadow=True)\n",
    "        plt.title(u\"特征影响占比\", fontproperties=font)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "north-socket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End\n"
     ]
    }
   ],
   "source": [
    "print(\"End\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "surprised-marriage",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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